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KeyboardInterrupt Traceback (most recent call last)
Cell In[39], line 5
2 from sklearn.cluster import SpectralClustering
4 mbk = SpectralClustering(n_clusters=8)
----> 5 mbk.fit(dfCleaned.drop('clarity', axis=1))
7 # view the labels
8 print(mbk.labels_)
File ~/Library/Python/3.9/lib/python/site-packages/sklearn/cluster/_spectral.py:750, in SpectralClustering.fit(self, X, y)
745 self.affinity_matrix_ = pairwise_kernels(
746 X, metric=self.affinity, filter_params=True, **params
747 )
749 random_state = check_random_state(self.random_state)
--> 750 self.labels_ = spectral_clustering(
751 self.affinity_matrix_,
752 n_clusters=self.n_clusters,
753 n_components=self.n_components,
754 eigen_solver=self.eigen_solver,
755 random_state=random_state,
756 n_init=self.n_init,
757 eigen_tol=self.eigen_tol,
758 assign_labels=self.assign_labels,
759 verbose=self.verbose,
760 )
761 return self
File ~/Library/Python/3.9/lib/python/site-packages/sklearn/cluster/_spectral.py:371, in spectral_clustering(affinity, n_clusters, n_components, eigen_solver, random_state, n_init, eigen_tol, assign_labels, verbose)
363 n_components = n_clusters if n_components is None else n_components
365 # We now obtain the real valued solution matrix to the
366 # relaxed Ncut problem, solving the eigenvalue problem
367 # L_sym x = lambda x and recovering u = D^-1/2 x.
368 # The first eigenvector is constant only for fully connected graphs
369 # and should be kept for spectral clustering (drop_first = False)
370 # See spectral_embedding documentation.
--> 371 maps = spectral_embedding(
372 affinity,
373 n_components=n_components,
374 eigen_solver=eigen_solver,
375 random_state=random_state,
376 eigen_tol=eigen_tol,
377 drop_first=False,
378 )
379 if verbose:
380 print(f"Computing label assignment using {assign_labels}")
File ~/Library/Python/3.9/lib/python/site-packages/sklearn/manifold/_spectral_embedding.py:314, in spectral_embedding(adjacency, n_components, eigen_solver, random_state, eigen_tol, norm_laplacian, drop_first)
312 laplacian *= -1
313 v0 = _init_arpack_v0(laplacian.shape[0], random_state)
--> 314 _, diffusion_map = eigsh(
315 laplacian, k=n_components, sigma=1.0, which="LM", tol=tol, v0=v0
316 )
317 embedding = diffusion_map.T[n_components::-1]
318 if norm_laplacian:
319 # recover u = D^-1/2 x from the eigenvector output x
File ~/Library/Python/3.9/lib/python/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py:1697, in eigsh(A, k, M, sigma, which, v0, ncv, maxiter, tol, return_eigenvectors, Minv, OPinv, mode)
1695 with _ARPACK_LOCK:
1696 while not params.converged:
-> 1697 params.iterate()
1699 return params.extract(return_eigenvectors)
File ~/Library/Python/3.9/lib/python/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py:560, in _SymmetricArpackParams.iterate(self)
558 else:
559 Bxslice = slice(self.ipntr[2] - 1, self.ipntr[2] - 1 + self.n)
--> 560 self.workd[yslice] = self.OPa(self.workd[Bxslice])
561 elif self.ido == 2:
562 self.workd[yslice] = self.B(self.workd[xslice])
File ~/Library/Python/3.9/lib/python/site-packages/scipy/sparse/linalg/_interface.py:232, in LinearOperator.matvec(self, x)
229 if x.shape != (N,) and x.shape != (N,1):
230 raise ValueError('dimension mismatch')
--> 232 y = self._matvec(x)
234 if isinstance(x, np.matrix):
235 y = asmatrix(y)
File ~/Library/Python/3.9/lib/python/site-packages/scipy/sparse/linalg/_eigen/arpack/arpack.py:944, in LuInv._matvec(self, x)
943 def _matvec(self, x):
--> 944 return lu_solve(self.M_lu, x)
File ~/Library/Python/3.9/lib/python/site-packages/scipy/linalg/_decomp_lu.py:147, in lu_solve(lu_and_piv, b, trans, overwrite_b, check_finite)
143 raise ValueError("Shapes of lu {} and b {} are incompatible"
144 .format(lu.shape, b1.shape))
146 getrs, = get_lapack_funcs(('getrs',), (lu, b1))
--> 147 x, info = getrs(lu, piv, b1, trans=trans, overwrite_b=overwrite_b)
148 if info == 0:
149 return x
KeyboardInterrupt:
